Spark Based Distributed Deep Learning Framework For Big Data Applications

Deep Learning architectures, such as deep neural networks, are currently the hottest emerging areas of data science, especially in Big Data. Deep Learning could be effectively exploited to address some major issues of Big Data, including withdrawing complex patterns from huge volumes of data, fast information retrieval, data classification, semantic indexing and so on. In this work, we designed and implemented a framework to train deep neural networks using Spark, fast and general data flow engine for large scale data processing. The design is similar to Google software framework called DistBelief which can utilize computing clusters with thousands of machines to train large scale deep networks. Training Deep Learning models requires extensive data and computation. Our proposed framework can accelerate the training time by distributing the model replicas, via stochastic gradient descent, among cluster nodes for data resided on HDFS.